Method for determining odorization parameters of smart gas device management and internet of things system thereof

ABSTRACT

The embodiments of the present disclosure provide method and Internet of Things system for determining odorization parameters of smart gas device management. The method may include obtaining gas data of a first gas sample at a first position of a smart gas pipeline network, odorizing at a second position of the smart gas pipeline network based on the odorization parameters, obtaining inspection data of a second gas sample at a third position of the smart gas pipeline network, and sending the inspection data to the smart gas pipeline network device parameter management sub-platform for analysis and processing, obtaining a target odorization concentration, and determining target odorization parameters, updating the odorization parameters based on the target odorization parameters, and send updated odorization parameters to the smart gas data center, and odorizing, based on the updated odorization parameters, at the second position of the smart gas pipeline network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.18/157,775, filed on Jan. 20, 2023, which claims priority of ChinesePatent Application No. 202211592133.3, filed on Dec. 13, 2022, theentire contents of which are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to a field of gas smart control, and inparticular, to a method and an Internet of Things system for determiningodorization parameters of smart gas device management.

BACKGROUND

The odorant may be configured to warn of gas leaks because of itsspecial odor. In order to ensure gas safety, an appropriate amount ofodorant needs to be added to the gas. At present, gas distributionstations mostly use large-displacement diaphragm pumps and piston pumpsto fill on time, or add odorant by gravity drip injection. Becausedisplacement of the diaphragm pumps and the piston pumps is relativelylarge, it is inconvenient to measure when the odorant with a relativelysmall flow is added. At the same time, in accordance with a certainodorization standard, odorization at a low peak of gas consumption maymake an odorization amount too large, resulting in excessive consumptionof odorant, which may increase the cost of odorization; and odorizationat a peak of gas consumption may make the odorization amount low becausethe odorization amount cannot be adjusted in time, which may not meet arequirement of a current national standard.

Therefore, it is desirable to provide a method and an Internet of Thingsystem for determining odorization parameters of smart gas devicemanagement, which can control an amount of added odorant timely andaccurately.

SUMMARY

According to one of the embodiments of the present disclosure, anInternet of Things system for determining odorization parameters ofsmart gas device management is provided. The Internet of Things systemcomprises a smart gas user platform, a smart gas service platform, asmart gas device management platform, a smart gas sensor networkplatform, and a smart gas object platform, wherein the smart gas devicemanagement platform includes a smart gas data center and a smart gaspipeline network device parameter management sub-platform, the smart gasobject platform is configured with a sampling device, an odorizationdevice, and an inspection device, and the smart gas pipeline networkdevice parameter management sub-platform is configured with a deviceparameter remote management module. The sampling device is configured toobtain gas data of a first gas sample at a first position of a smart gaspipeline network and transmit the gas data to the smart gas data centerthrough the smart gas sensor network platform, wherein the firstposition is a sampling position. The odorization device is configured toodorize at a second position of the smart gas pipeline network based onthe odorization parameters sent by the smart gas data center, whereinthe second position is an odorization position. The inspection device isconfigured to obtain inspection data of a second gas sample at a thirdposition of the smart gas pipeline network and transmit the inspectiondata to the smart gas data center through the smart gas sensor networkplatform, and the smart gas data center sends the inspection data to thesmart gas pipeline network device parameter management sub-platform foranalysis and processing, wherein a distance between the second positionand the third position is greater than a first threshold, wherein thethird position is a detection position, and the first threshold is asystem default value. The device parameter remote management module isconfigured to: obtain a target odorization concentration; determinetarget odorization parameters by processing the target odorizationconcentration and the inspection data through a parameter determinationmodel, wherein the parameter determination model is a machine learningmodel, wherein the parameter determination model is obtained by reversetraining, and the reverse training includes: obtaining historicalodorization concentrations and historical inspection data correspondingto different odorization parameters in a historical odorization processas a first training sample, wherein the different odorization parametersare used as a first label of the first training sample; and obtainingthe parameter determination model by training based on the firsttraining sample and the first label; update the odorization parametersbased on the target odorization parameters, and send updated odorizationparameters to the smart gas data center, and the smart gas data centersends the updated odorization parameters to the smart gas objectplatform through the smart gas sensor network platform. The odorizationdevice is configured to odorize at the second position of the smart gaspipeline network based on the updated odorization parameters.

According to one of the embodiments of the present disclosure, a methodfor determining odorization parameters of smart gas device management isprovided. The method is implemented by an Internet of Things system fordetermining the odorization parameters of smart gas device management.The Internet of Things system includes a smart gas user platform, asmart gas service platform, a smart gas device management platform, asmart gas sensor network platform, and a smart gas object platform, thesmart gas device management platform includes a smart gas data centerand a smart gas pipeline network device parameter managementsub-platform, the smart gas object platform is configured with asampling device, an odorization device, and an inspection device, thesmart gas pipeline network device parameter management sub-platform isconfigured with a device parameter remote management module. The methodcomprises: obtaining gas data of a first gas sample at a first positionof a smart gas pipeline network and transmitting the gas data to thesmart gas data center through the smart gas sensor network platformbased on the sampling device, wherein the first position is a samplingposition; odorizing at a second position of the smart gas pipelinenetwork based on odorization parameters sent by the smart gas datacenter through the odorization device, wherein the second position is anodorization position; obtaining inspection data of a second gas sampleat a third position of the smart gas pipeline network and transmittingthe inspection data to the smart gas data center through the smart gassensor network platform based on the inspection device, and the smartgas data center sending the inspection data to the smart gas pipelinenetwork device parameter management sub-platform for analysis andprocessing, wherein a distance between the second position and the thirdposition is greater than a first threshold, wherein the third positionis a detection position, and the first threshold is a system defaultvalue; obtaining a target odorization concentration through the deviceparameter remote management module, and determining target odorizationparameters by processing the target odorization concentration and theinspection data through a parameter determination model, wherein theparameter determination model is a machine learning model, wherein theparameter determination model is obtained by reverse training, and thereverse training includes: obtaining historical odorizationconcentrations and historical inspection data corresponding to differentodorization parameters in a historical odorization process as a firsttraining sample, wherein the different odorization parameters are usedas a first label of the first training sample; and obtaining theparameter determination model by training based on the first trainingsample and the first label; updating the odorization parameters based onthe target odorization parameters through the device parameter remotemanagement module, and send updated odorization parameters to the smartgas data center, and the smart gas data center sends the updatedodorization parameters to the smart gas object platform through thesmart gas sensor network platform; and odorizing, based on the updatedodorization parameters, at the second position of the smart gas pipelinenetwork through the odorization device.

According to one of the embodiments of the present disclosure, anon-transitory computer-readable storage medium storing computerinstructions is provided. When reading the computer instructions in thestorage medium, a computer may implement the method for determiningodorization parameters of smart gas device management.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further illustrated in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures, wherein:

FIG. 1 is a structure diagram illustrating an exemplary Internet ofThings system for controlling automatic odorization of smart gas devicemanagement according to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating an exemplary Internet ofThings for controlling automatic odorization of smart gas devicemanagement according to some embodiments of the present disclosure;

FIG. 3 is a flowchart illustrating an exemplary process of a method forcontrolling automatic odorization of smart gas device managementaccording to some embodiments of the present disclosure;

FIG. 4 is an exemplary schematic diagram illustrating determining targetodorization parameters according to some embodiments of the presentdisclosure;

FIG. 5 is an exemplary schematic diagram illustrating obtaining aremaining concentration according to some embodiments of the presentdisclosure; and

FIG. 6 is an exemplary schematic diagram illustrating trainingprediction model according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In order to more clearly illustrate the technical solutions related tothe embodiments of the present disclosure, a brief introduction of thedrawings referred to the description of the embodiments is providedbelow. Obviously, the drawings described below are only some examples orembodiments of the present disclosure. Those having ordinary skills inthe art, without further creative efforts, may apply the presentdisclosure to other similar scenarios according to these drawings.Unless obviously obtained from the context or the context illustratesotherwise, the same numeral in the drawings refers to the same structureor operation.

It should be understood that the “system,” “device,” “unit,” and/or“module” used herein are one method to distinguish different components,elements, parts, sections, or assemblies of different levels. However,if other words can achieve the same purpose, the words can be replacedby other expressions.

As used in the disclosure and the appended claims, the singular forms“a,” “an,” and “the” include plural referents unless the content clearlydictates otherwise; the plural forms may be intended to include singularforms as well. In general, the terms “comprise,” “comprises,” and/or“comprising,” “include,” “includes,” and/or “including,” merely promptto include steps and elements that have been clearly identified, andthese steps and elements do not constitute an exclusive listing. Themethods or devices may also include other steps or elements.

The flowcharts used in the present disclosure illustrate operations thatthe system implements according to the embodiment of the presentdisclosure. It should be understood that the foregoing or followingoperations may not necessarily be performed exactly in order. Instead,the operations may be processed in reverse order or simultaneously.Besides, one or more other operations may be added to these processes,or one or more operations may be removed from these processes.

FIG. 1 is a structure diagram illustrating an exemplary Internet ofThings system for controlling automatic odorization of smart gas devicemanagement according to some embodiments of the present disclosure.

As shown in FIG. 1 , the Internet of Things system 100 may include asmart gas user platform, a smart gas service platform, a smart gasdevice management platform, a smart gas sensor network platform, and asmart gas object platform interacting in turn. The smart gas userplatform, the smart gas service platform, the smart gas devicemanagement platform, the smart gas sensor network platform, and thesmart gas object platform may interact by communication connection inturn.

The smart gas user platform may be a platform for interacting withusers. In some embodiments, the smart gas user platform may beconfigured as a terminal device. For example, the terminal device mayinclude a smart electronic device that implement data processing as wellas data communication such as a laptop computer, a cell phone, etc.,which will not be overly limited herein. In some embodiments, the smartgas user platform may be configured to receive gas device operation andmanagement information transmitted by the smart gas service platform,and may further be configured to transmit the received gas deviceoperation and management information query instructions to the smart gasservice platform.

In some embodiments, the smart gas user platform may include a gas usersub-platform, a government user sub-platform, and a supervisory usersub-platform so that a gas user, a government user, and a supervisoryuser may all receive the gas device operation and management informationtransmitted by the smart gas service platform. The gas user may be auser that uses gas. The government user may be a user that provides gasoperation services. The supervisory user may be a user that supervisessafety of gas usage. In some embodiments, the gas user sub-platform maybe configured to receive gas device-related information, such as gasdevice maintenance, a gas usage safety reminder, etc. transmitted by ansmart gas service sub-platform, and may also be configured to transmitgas usage query instructions to the smart gas service sub-platform. Insome embodiments, the government user sub-platform may receiveinformation related to gas operation, such as information on whether toadd odorant, transmitted by a smart operation service sub-platform. Insome embodiments, the supervisory user sub-platform may be configured toreceive the gas device operation and management information, gas safetyoperation information, etc. such as information on addition of odorant,transmitted by a smart supervisory service sub-platform, and may furtherbe configured to transmit safety management query instructions or gaspipeline network abnormality query instructions to the smart supervisoryservice sub-platform.

The smart gas service platform may be a platform for receiving andtransmitting data and/or information. In some embodiments, the smart gasservice platform may be configured to receive the gas device operationand management information transmitted by the smart gas data center inthe smart gas device management platform, and transmit the gas deviceoperation and management information to the smart gas user platform. Thesmart gas service platform may be also used to receive the gas deviceoperation and management information query instructions issued by thesmart gas user platform and transmit the gas device operation andmanagement information query instructions to the smart gas data center.

In some embodiments, the smart gas service platform may include thesmart gas service sub-platform, the smart operation servicesub-platform, and the smart supervisory service sub-platform. The smartgas service sub-platform, the smart operation service sub-platform, andthe smart supervisory service sub-platform may interact with the gasuser sub-platform, the government user sub-platform, and the supervisoryuser sub-platform correspondingly to receive query instructions issuedby the corresponding user sub-platforms and transmit the queryinstructions to the smart gas data center, receive the gas deviceoperation and management information transmitted by the smart gas datacenter and transmit the gas device operation and management informationto the corresponding user sub-platforms.

The smart gas device management platform may be configured tocoordinate, manage, and analyze gas-related data (e.g., gas data andinspection data, etc.) in the smart gas pipeline network. In someembodiments, the smart gas device management platform may receive,analyze, and process the gas-related data uploaded by the smart gassensor network platform, and send the processed data to the smart gasobject platform through the smart gas sensor network platform.

In some embodiments, the smart gas device management platform mayinclude a smart gas data center, a smart gas indoor device parametermanagement sub-platform, and a smart gas pipeline network deviceparameter management sub-platform. Both the smart gas pipeline networkdevice parameter management sub-platform and the smart gas indoor deviceparameter management sub-platform may include a device operationparameter inspection and warning module and a device parameter remotemanagement module. The device operation parameter inspection and warningmodule may be configured to view historical data and real-time data ofdevice operation parameters in the smart gas pipeline network, inspectand warn according to a preset threshold, and may further be configuredto initiate warning messages. The device parameter remote managementmodule may be configured to remotely set and adjust the device parameterof the smart gas object platform, and remotely authorize the deviceparameter adjustment initiated by the smart gas object platform on site.

In some embodiments, the smart gas data center may receive thegas-related data in the smart gas pipeline network transmitted by thesmart gas sensor network platform and send the gas-related data to thesmart gas pipeline network device parameter management sub-platform foranalysis and processing. For example, the smart gas pipeline networkdevice parameter management sub-platform may be configured with a deviceparameter remote management module. The device parameter remotemanagement module may process the gas-related data, determine and updatethe odorization parameters, send the updated odorization parameters tothe smart gas data center, and send the updated odorization parametersto the smart gas object platform through the smart gas sensor networkplatform. The smart gas data center may further receive gas deviceoperation and management information query instruction transmitted bythe smart gas service platform and transmit the gas device operation andmanagement information query instruction to the smart gas sensor networkplatform.

The smart gas sensor network platform configured as a communicationnetwork and a gateway may be configured to receive the gas-related datain the smart gas pipeline network obtained by the smart gas objectplatform and transmit the gas-related data to the smart gas data center.The smart gas sensor network platform may further receive the gas deviceoperation and management information query instruction and theodorization parameters transmitted by the smart gas data center andtransmit the gas device operation and management information queryinstruction and the odorization parameters to the smart gas objectplatform.

In some embodiments, the smart gas sensor network platform may include asmart gas indoor device sensor network sub-platform and a smart gaspipeline network device sensor network sub-platform. The smart gasindoor device sensor network sub-platform and the smart gas pipelinenetwork device sensor network sub-platform may receive the gas deviceoperation and management information query instruction from the smartgas data center and transmit the gas device operation and managementinformation query instruction to the smart gas indoor device objectsub-platform and the smart gas pipeline network device objectsub-platform, respectively.

The smart gas object platform may be configured to obtain thegas-related data in the smart gas pipeline network. For example, thesmart gas object platform may be equipped with a sampling device, anodorization device, and an inspection device. The sampling device may beconfigured to obtain gas data. The odorization device may be configuredto odorize the gas. The inspection device may be configured to obtaininspection data. In some embodiments, the smart gas object platform mayalso be configured to receive the gas device operation and managementinformation query instruction transmitted by the smart gas sensornetwork platform. In some embodiments, the smart gas object platform maybe configured as various types of gas devices, including an indoordevices, a pipeline network device, etc. The indoor device may be adevice contained by the place of gas user. The pipeline network devicemay be a device contained in the gas pipeline network, for example, anodorization device, a gas pipeline, a gas flow meter, a valve controldevice, a thermometer, a barometer, etc.

It should be understood that the system and the modules thereof shown inFIG. 1 may be implemented in various ways.

It should be noted that the above description of the Internet of Thingssystem 100 and the modules thereof is merely for convenience ofillustration and not intended to limit the present disclosure to thescope of the embodiments. It is understood that for those skilled in theart, after understanding the principle of the system, it may be possibleto arbitrarily combine various modules or form a sub-system to connectwith other modules without departing from this principle. In someembodiments, the smart gas user platform, the smart gas serviceplatform, the smart gas device management platform, the smart gas sensornetwork platform, and the smart gas object platform disclosed in FIG. 1may be different modules in one system or one module implementing thefunctions of the two or more modules. For example, each module may sharea storage module, and each module may have its storage module. Suchvariations are within the scope of protection of the present disclosure.

FIG. 3 is a flowchart illustrating an exemplary process of a method forcontrolling automatic odorization of smart gas device managementaccording to some embodiments of the present disclosure.

In 310, obtaining gas data of a first gas sample at a first position ofa smart gas pipeline network and transmitting the gas data to the smartgas data center through the smart gas sensor network platform based on asampling device.

The sampling device may be a device configured to obtain a gas sampleand gas data thereof before odorization. In some embodiments, thesampling device may include a sensor for obtaining the gas data, such asa gas flow sensor for obtaining a gas flow, a temperature sensor forobtaining a gas temperature, etc. In some embodiments, an installationposition of the sampling device may be set manually. As shown in FIG. 2, the sampling device 210 may be configured at a first position of thesmart gas pipeline network.

The smart gas pipeline network may be a gas integrated managementpipeline network constructed based on advanced technologies such as theInternet of Things, big data, cloud computing, and mobile Internet,which may realize the inspection and management of data such as gaspressure, flow, confined space, gas leakage, etc.

The first position may be a position where the sampling device takes asample in the smart gas pipeline network. The first position may be setmanually.

The first gas sample may be a gas sample taken by the sampling devicefrom the first position. In some embodiments, the first gas sample maybe a gas sample without odorization, which does not contain an odorant.

The gas data may be data that reflects relevant gas information in thefirst gas sample, for example, the gas data may include gas flow, gasflow rate, gas concentration, gas pressure, gas temperature, or thelike, or any combination thereof. The gas data may be obtained based ona sensor configured within the sampling device, for example, the gasflow may be obtained based on a gas flow sensor.

In some embodiments, the gas data obtained by the sampling device may betransmitted to the smart gas data center through the smart gas sensornetwork platform.

In 320, odorizing at a second position of the smart gas pipeline networkbased on odorization parameters sent by the smart gas data centerthrough an odorization device.

The odorization device may be a device configured to add an odorant tothe gas in the smart gas pipeline network. For example, the odorizationdevice may perform, based on odorization parameters (e.g., anodorization time, an odorization position, an odorization amount, etc.),odorization of a corresponding dose at a corresponding time. In someembodiments, an installation position of the odorization device may beset manually. As shown in FIG. 2 , the odorization device 220 may beconfigured at a second position of the smart gas pipeline network.

The second position may be a position of the odorization device toodorize in the smart gas pipeline network. In some embodiments, thesecond position may be located after the first position. As shown inFIG. 2 , the second position may be at a downstream node of the firstposition. The second position may be set manually.

The odorization parameters may be parameters corresponding to theodorization device for odorizing. For example, the odorizationparameters may include a type of odorant, an odorization amount, anodorization frequency, an odorization time, or the like, or anycombination thereof.

In some embodiments, the odorization parameters may be manually preset.For example, in a first odorization operation, the odorization amountmay be manually preset to 1 L. The odorization time may be manuallypreset to 13:30, etc. In some embodiments, the odorization device maydetermine, based on an effect of a previous odorization, whether toupdate the odorization parameters and perform, based on the updatedodorization parameters, subsequent odorization. For example, if theinspection data of the gas after the first odorization shows that theodorant concentration in the gas is too low compared with an odorizationconcentration standard value, the second odorization may be performedbased on the updated odorization parameters. The odorizationconcentration standard value may be an odorant concentration that may beperceived by the human body when the gas leakage in the air reaches aprescribed percentage of a lower explosion limit (such as 20%, differenttypes of gas may have different lower explosion limits). The odorizationconcentration standard value may be obtained in various ways such asquerying a database or calculation based on information such as a typeof odorant (e.g., tetrahydrothiophene, mercaptan, etc.), the type of gas(e.g., natural gas, liquefied petroleum gas, etc.), etc.

In 330, obtaining inspection data of a second gas sample at a thirdposition of the smart gas pipeline network and transmitting theinspection data to the smart gas data center through the smart gassensor network platform based on the inspection device, and the smartgas data center sending the inspection data to the smart gas pipelinenetwork device parameter management sub-platform for analysis andprocessing.

The inspection device may be a device configured to obtain a gas sampleafter odorization and inspection data thereof. In some embodiments, theinspection device may include a sensor for obtaining the inspectiondata, such as an odorant concentration sensor for obtaining the odorantconcentration in the gas, etc. In some embodiments, the installationposition of the inspection device may be set manually. As shown in FIG.2 , an inspection device 230 may be configured at a third position ofthe smart gas pipeline network.

The third position may be a position where the inspection device takesand detects a sample in the smart gas pipeline network. In someembodiments, the third position may be located after the secondposition. As shown in FIG. 2 , the third position may be at a downstreamnode of the second position. The third position may be set manually.

In some embodiments, a distance between the second position and thethird position may be greater than a first threshold value. The firstthreshold value may be a system default value, an empirical value, amanually pre-set value, or the like, or any combination thereof, whichmay be set according to an actual need.

In some embodiments of the present disclosure, the distance between thesecond position and the third position may be greater than the firstthreshold, which may ensure that a spacing between the odorizationdevice and the inspection device is sufficiently far apart to avoid aproblem of uneven and insufficient mixing of odorant and gas in thedetected second gas sample, thereby obtaining accurate inspection data.

The second gas sample may be a gas sample taken by the inspection devicefrom the third position. In some embodiments, the second gas sample maybe an odorized gas sample containing an odorant.

The inspection data may be data that reflect relevant gas information inthe second gas sample. For example, the inspection data may include anodorant concentration in the gas, a gas flow rate, a gas concentration,a gas pressure, a gas temperature, any combination thereof. Theinspection data may be obtained based on a sensor configured in theinspection device. For example, the odorant concentration in the gas maybe obtained based on an odorant concentration sensor.

In 340, updating, based on the inspection data, the odorizationparameters through the device parameter remote management module andsending updated odorization parameters to the smart gas data center, andthe smart gas data center sending the updated odorization parameters tothe smart gas object platform through the smart gas sensor networkplatform.

The device parameter remote management module may be a device configuredto manage and control odorization operations. In some embodiments, thedevice parameter remote management module may update the odorizationparameters based on the inspection data, transmit the updatedodorization parameters to the smart gas data center, and then furthersend the updated odorization parameters to the odorization device,thereby realizing the control of the odorization process.

In some embodiments, the device parameter remote management module mayupdate the odorization parameters based on the inspection data invarious ways. For example, when the inspection data shows that theodorant concentration in the gas is lower than the odorizationconcentration standard value, the device parameter remote managementmodule may increase the odorization amount in the odorization parametersappropriately.

In some embodiments, in order to update the odorization parameters basedon the inspection data, the device parameter remote management modulemay be also configured to obtain a target odorization concentration;determine target odorization parameters based on the target odorizationconcentration and the inspection data; and update the odorizationparameters based on the target odorization parameters.

The target odorization concentration may be an ideal value of odorantconcentration after odorizing the gas. In the case of the targetodorization concentration, if the gas leaks into the air, an odor of theodorant may make people aware of the danger in time before a dangeroccurs.

In some embodiments, the device parameter remote management module maydetermine the target odorization concentration based on the odorizationconcentration standard value. For example, the odorization concentrationstandard value may be used directly as the target odorizationconcentration, or a value of the odorization concentration standardvalue increased by a certain percentage (e.g., 1%) may be used as thetarget odorization concentration.

In some embodiments, the device parameter remote management module mayobtain the odorization concentration standard value and the remainingconcentration and determine the target odorization concentration basedon the odorization concentration and the remaining concentration. Moredescriptions regarding the odorization concentration standard value maybe found in the operation 330 and relevant descriptions thereof.

The remaining concentration may refer to a tolerated odorantconcentration to ensure safety on the basis that the odorizationconcentration reaches the odorization concentration standard value. Inan actual process of odorization, in order to ensure that gas leakagedoes not cause danger to personal and property safety, the odorantconcentration may need to be increased based on the odorizationconcentration standard value. The increased concentration value may bethe remaining concentration. For example, the odorization concentrationstandard value may be 20 mg/m³. The remaining concentration may be 0.8mg/m³. More descriptions regarding the remaining concentration may befound in FIG. 5 and relevant descriptions thereof.

In some embodiments, the target odorization concentration may bedetermined based on the odorization concentration standard value and theremaining concentration. For example, the target odorizationconcentration may be obtained by adding the odorization concentrationstandard value to the remaining concentration.

In some embodiments, the device parameter remote management module mayfurther determine the target odorization parameters by processing thetarget odorization concentration and the inspection data through aparameter determination model.

As shown in FIG. 4 , an input 410 of a parameter determination model 420may include a target odorization concentration 411 and inspection data412. An output of the parameter determination model 420 may be a targetodorization parameter 430.

In some embodiments, the parameter determination model may be a machinelearning model. For example, the parameter determination model may be adeep neural networks (DNN) model, a recurrent neural networks (RNN)model, or the like, or any combination thereof.

In some embodiments, the parameter determination model may be obtainedby reverse training. As shown in FIG. 4 , a trained parameterdetermination model 420 may be obtained by training an initial parameterdetermination model 450 based on a first training sample and a firstlabel 440.

In some embodiments, the first training sample may include historicalodorization concentrations and historical inspection data correspondingto different odorization parameters in a historical odorization. Thefirst label may include each of the odorization parameters. The firsttraining sample and the first label may be determined by retrievinghistorical data.

In some embodiments of the present disclosure, determining the targetodorization parameters through the parameter determination model maymake the target odorization parameters more accurate and may effectivelyreduce a duration required to determine the target odorizationparameters, thereby improving the operation efficiency of the Internetof Things system for controlling automatic odorization of smart gasdevice management.

In 350, odorizing, based on the updated odorization parameters, at thesecond position of the smart gas pipeline network through theodorization device.

In some embodiments, the odorization device may perform, based on theupdated odorization parameters (e.g., an updated odorization time, anupdated odorization amount, etc.) obtained from the smart gas datacenter, odorization of a corresponding dose at a corresponding time.

In some embodiments of the present disclosure, by updating theodorization parameters through the inspection data, the odorizationparameters may be updated in real time based on the effect of historicalodorization, thereby continuously adjusting the entire odorizationprocess, so that the odorant concentration in the gas can reach acurrent national standard and waste of odorant can be reduced.

FIG. 5 is an exemplary schematic diagram illustrating obtaining aremaining concentration according to some embodiments of the presentdisclosure.

In some embodiments, the remaining concentration may be determined basedon an odorization concentration standard value and inspection data.

As shown in FIG. 5 , the device parameter remote management module mayconstruct a first feature vector 520 based on the odorizationconcentration standard value 511 and the inspection data 512.

The first feature vector may be a vector that reflects a data feature ofthe odorization concentration standard value and the inspection data.The first feature vector may be determined based on the odorizationconcentration standard value and the inspection data in a previousodorization process, for example, the odorization concentration standardvalue and the inspection data may be used as each element value of thefirst feature vector, respectively. Exemplarily, in the previousodorization process, if the odorization concentration standard value is20 mg/m³, and the inspection data includes an odorant concentration of18 mg/m³, a gas flow rate of 2 L/s, a gas temperature of 29° C., . . . ,then the first feature vector may be constructed as (20 mg/m³, 18 mg/m³,2 L/s, 29° C., . . . ).

As shown in FIG. 5 , the device parameter remote management module maydetermine a reference feature vector 530 based on the first featurevector 520.

In some embodiments, a way to determine the reference feature vector mayinclude: forming a feature vector library by constructing a plurality ofhistorical feature vectors with the same elements as the first featurevector based on the odorization concentration standard value during thehistorical odorization process and the corresponding historicalinspection data, and determining, based on the first feature vector, thehistorical feature vector with a highest similarity from the featurevector library as the reference feature vector. Exemplarily, a way tocalculate the similarity may include but is not limited to, a cosinesimilarity, a Euclidean distance, a Pearson correlation coefficient,etc.

As shown in FIG. 5 , the device parameter remote management module maydetermine a predicted standard deviation 550 of the odorantconcentration after odorization based on the odorization parameters byprocessing the reference feature vector 530 through a prediction model540.

In some embodiments, there may be a difference between the odorantconcentration in the gas and the target odorization concentration afterodorization based on the odorization parameters. The predicted standarddeviation of the odorant concentration may be a predicted value thatreflects the difference between the odorant concentration in the gas andthe target odorization concentration. The larger the predicted standarddeviation of the odorant concentration is, the larger the differencebetween the odorant concentration and the target odorizationconcentration is.

The prediction model may be configured to determine the predictedstandard deviation of the odorant concentration after odorization basedon the odorization parameters. An input of the prediction model may bethe reference feature vector and an output of the prediction model maybe the predicted standard deviation of the odorant concentration afterodorization based on the odorization parameters. In some embodiments,the prediction model may be a machine learning model, such as a neuralnetwork model, etc.

The prediction model may be obtained by training. More descriptionsregarding the training process of the prediction model may be found inFIG. 7 and relevant descriptions thereof.

As shown in FIG. 5 , the device parameter remote management module maydetermine a remaining concentration 560 based on the predicted standarddeviation 550 of the odorant concentration.

In some embodiments, the device parameter remote management module maydetermine the remaining concentration based on the predicted standarddeviation in a variety of ways. For example, the device parameter remotemanagement module may determine the remaining concentration based on thepredicted standard deviation by a conventional calculation manner, sothat the target odorization concentration may be higher than theodorization concentration standard value in most cases (e.g., 99% of thecases).

In some embodiments of the present disclosure, based on the firstfeature vector, the feature vector with a smallest distance from thefirst feature vector may be determined from the feature vector libraryas the reference feature vector, and the reference feature vector may beused as the input of the prediction model, so that data most similar toa current situation in the historical odorization data may be used asthe reference for the model prediction, thereby improving the accuracyof the prediction model and obtaining a more accurate predicted standarddeviation of the odorant concentration, so as to further determine amore accurate remaining concentration.

FIG. 6 is an exemplary schematic diagram illustrating trainingprediction model according to some embodiments of the presentdisclosure.

As shown in FIG. 6 , a training process of the prediction model 690 mayinclude the following operations.

A plurality of sample data 610 may be obtained. Each sample data 610 maybe historical inspection data on which an odorization operation isperformed.

A cluster center set 620 may be obtained by clustering the plurality ofsample data 610.

For example, the plurality of sample data 610 may be clustered by aclustering algorithm to determine the cluster center set 620. Thecluster center set may include one or more cluster centers. For example,features of a plurality of sample data may be extracted to determinefeature vectors of the plurality of sample data, and the feature vectorsof the sample data may be clustered by a clustering algorithm to obtainthe cluster center set. The cluster center set may include clustercenters I and II. Exemplarily, a way to perform feature extraction mayinclude but is not limited to, a multilayer perceptron, a convolutionalneural network, a residual network, etc. The clustering algorithm mayinclude but is not limited to, a k-means clustering algorithm, adensity-based spatial clustering of applications with noise (DBSCAN)algorithm, etc.

For each of the plurality of sample data 610, the target cluster center640 may be matched in the cluster center set 620 based on the featurevector 630 corresponding to each sample data 610, and a center vector650 corresponding to the target cluster center 640 may be used as ahistorical feature vector 671. The target cluster center may be acluster center closest to a feature vector corresponding to a certainsample data in the cluster center set. In some embodiments, for eachcluster center, a mean value vector of the feature vectors of all thesample data in the cluster center may be determined as the center vectorof the cluster center.

A standard deviation 681 of the odorant concentration may be determinedbased on an odorant concentration distribution feature 660 in theplurality of sample data 610.

The prediction model 690 may be trained by using the plurality ofhistorical feature vectors 671 as training samples 670 and using thecorresponding standard deviation 681 of the odorant concentration as acorresponding label 680.

For example, the training sample 670 and the label 680 may be input toan initial prediction model, a loss function may be established based onthe label 680 and an output result of the initial prediction model,parameters of the initial prediction model may be updated, and trainingof the model may be completed to obtain a trained prediction model whenthe loss function satisfies a preset condition. The preset condition maybe that the loss function converges, a count of iterations reaches athreshold value, etc.

In some embodiments of the present disclosure, in the training processof the prediction model, clustering may be performed on the sample data,the target cluster centers may be determined based on the clusteringresults, and then the training data and the label may be determined, andthe historical feature vectors corresponding to the sample data of asame cluster may be used as input of the prediction model, therebyreducing the cost of obtaining the feature vectors and reducing theamount of data processing.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Although not explicitly stated here,those skilled in the art may make various modifications, improvementsand amendments to the present disclosure. These alterations,improvements, and modifications are intended to be suggested by thisdisclosure, and are within the spirit and scope of the exemplaryembodiments of this disclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various parts of this specification are not necessarilyall referring to the same embodiment. In addition, some features,structures, or features in the present disclosure of one or moreembodiments may be appropriately combined.

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. However, thisdisclosure does not mean that the present disclosure object requiresmore features than the features mentioned in the claims. Rather, claimedsubject matter may lie in less than all features of a single foregoingdisclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the present disclosureare to be understood as being modified in some instances by the term“about,” “approximate,” or “substantially.” For example, “about,”“approximate,” or “substantially” may indicate ±20% variation of thevalue it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the present disclosure are approximations, thenumerical values set forth in the specific examples are reported asprecisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of the presentdisclosure disclosed herein are illustrative of the principles of theembodiments of the present disclosure. Other modifications that may beemployed may be within the scope of the present disclosure. Thus, by wayof example, but not of limitation, alternative configurations of theembodiments of the present disclosure may be utilized in accordance withthe teachings herein. Accordingly, embodiments of the present disclosureare not limited to that precisely as shown and described.

What is claimed is:
 1. An Internet of Things system for determiningodorization parameters of smart gas device management, comprising asmart gas user platform, a smart gas service platform, a smart gasdevice management platform, a smart gas sensor network platform, and asmart gas object platform, wherein the smart gas device managementplatform includes a smart gas data center and a smart gas pipelinenetwork device parameter management sub-platform, the smart gas objectplatform is configured with a sampling device, an odorization device,and an inspection device, and the smart gas pipeline network deviceparameter management sub-platform is configured with a device parameterremote management module, wherein the sampling device is configured toobtain gas data of a first gas sample at a first position of a smart gaspipeline network and transmit the gas data to the smart gas data centerthrough the smart gas sensor network platform, wherein the firstposition is a sampling position; the odorization device is configured toodorize at a second position of the smart gas pipeline network based onthe odorization parameters sent by the smart gas data center, whereinthe second position is an odorization position; the inspection device isconfigured to obtain inspection data of a second gas sample at a thirdposition of the smart gas pipeline network and transmit the inspectiondata to the smart gas data center through the smart gas sensor networkplatform, and the smart gas data center sends the inspection data to thesmart gas pipeline network device parameter management sub-platform foranalysis and processing, wherein a distance between the second positionand the third position is greater than a first threshold, wherein thethird position is a detection position, and the first threshold is asystem default value; the device parameter remote management module isconfigured to: obtain a target odorization concentration; determinetarget odorization parameters by processing the target odorizationconcentration and the inspection data through a parameter determinationmodel, wherein the parameter determination model is a machine learningmodel, wherein the parameter determination model is obtained by reversetraining, and the reverse training includes: obtaining historicalodorization concentrations and historical inspection data correspondingto different odorization parameters in a historical odorization processas a first training sample, wherein the different odorization parametersare used as a first label of the first training sample; and obtainingthe parameter determination model by training based on the firsttraining sample and the first label; update the odorization parametersbased on the target odorization parameters, and send updated odorizationparameters to the smart gas data center, and the smart gas data centersends the updated odorization parameters to the smart gas objectplatform through the smart gas sensor network platform; and theodorization device is configured to odorize at the second position ofthe smart gas pipeline network based on the updated odorizationparameters.
 2. The Internet of Things system for determining odorizationparameters of smart gas device management of claim 1, wherein to obtainthe target odorization concentration, the device parameter remotemanagement module is further configured to: obtain an odorizationconcentration standard and a remaining concentration; and determine thetarget odorization concentration based on the odorization concentrationstandard and the remaining concentration.
 3. The Internet of Thingssystem for determining odorization parameters of smart gas devicemanagement of claim 2, wherein to obtain the remaining concentration,the device parameter remote management module is further configured to:construct a first feature vector based on the odorization concentrationstandard and the inspection data; determine a reference feature vectorbased on the first feature vector; determine a predicted standarddeviation of an odorant concentration after odorization based on theodorization parameters by processing the reference feature vectorthrough a prediction model, wherein the prediction model is a machinelearning model; and determine the remaining concentration based on thepredicted standard deviation of the odorant concentration.
 4. TheInternet of Things system for determining odorization parameters ofsmart gas device management of claim 3, wherein the prediction model isobtained by a training process including: obtaining a plurality ofsample data, wherein each sample data is a historical inspection data onwhich an odorization operation is performed; and obtaining a clustercenter set by clustering the plurality of sample data; for each of theplurality of sample data, matching a target cluster center in thecluster center set based on a feature vector corresponding to eachsample data and using a center vector corresponding to the targetcluster center as a historical feature vector; determining a standarddeviation of the odorant concentration based on an odorant concentrationdistribution feature in the plurality of sample data; and obtaining theprediction model by training using a plurality of historical featurevectors as a plurality of sets of training data and using the standarddeviation of the odorant concentration as a training label.
 5. TheInternet of Things system for determining odorization parameters ofsmart gas device management of claim 1, wherein the gas data includesone or more of a gas flow, a gas flow rate, a gas concentration, a gaspressure, a gas temperature.
 6. The Internet of Things system fordetermining odorization parameters of smart gas device management ofclaim 1, wherein the odorization parameters include one or more of atype of an odorant, an odorization amount, an odorization frequency, anodorization time.
 7. The Internet of Things system for determiningodorization parameters of smart gas device management of claim 1,wherein the inspection data includes one or more of the odorantconcentration in gas, a gas flow rate, a gas concentration, a gaspressure, a gas temperature.
 8. A method for determining odorizationparameters of smart gas device management, implemented by an Internet ofThings system for determining the odorization parameters of smart gasdevice management, wherein the Internet of Things system includes asmart gas user platform, a smart gas service platform, a smart gasdevice management platform, a smart gas sensor network platform, and asmart gas object platform, the smart gas device management platformincludes a smart gas data center and a smart gas pipeline network deviceparameter management sub-platform, the smart gas object platform isconfigured with a sampling device, an odorization device, and aninspection device, the smart gas pipeline network device parametermanagement sub-platform is configured with a device parameter remotemanagement module, and the method comprises: obtaining gas data of afirst gas sample at a first position of a smart gas pipeline network andtransmitting the gas data to the smart gas data center through the smartgas sensor network platform based on the sampling device, wherein thefirst position is a sampling position; odorizing at a second position ofthe smart gas pipeline network based on odorization parameters sent bythe smart gas data center through the odorization device, wherein thesecond position is an odorization position; obtaining inspection data ofa second gas sample at a third position of the smart gas pipelinenetwork and transmitting the inspection data to the smart gas datacenter through the smart gas sensor network platform based on theinspection device, and the smart gas data center sending the inspectiondata to the smart gas pipeline network device parameter managementsub-platform for analysis and processing, wherein a distance between thesecond position and the third position is greater than a firstthreshold, wherein the third position is a detection position, and thefirst threshold is a system default value; obtaining a targetodorization concentration through the device parameter remote managementmodule, and determining target odorization parameters by processing thetarget odorization concentration and the inspection data through aparameter determination model, wherein the parameter determination modelis a machine learning model, wherein the parameter determination modelis obtained by reverse training, and the reverse training includes:obtaining historical odorization concentrations and historicalinspection data corresponding to different odorization parameters in ahistorical odorization process as a first training sample, wherein thedifferent odorization parameters are used as a first label of the firsttraining sample; and obtaining the parameter determination model bytraining based on the first training sample and the first label;updating the odorization parameters based on the target odorizationparameters through the device parameter remote management module, andsend updated odorization parameters to the smart gas data center, andthe smart gas data center sends the updated odorization parameters tothe smart gas object platform through the smart gas sensor networkplatform; and odorizing, based on the updated odorization parameters, atthe second position of the smart gas pipeline network through theodorization device.
 9. The method for determining odorization parametersof smart gas device management of claim 8, wherein the obtaining thetarget odorization concentration further includes: obtaining anodorization concentration standard and a remaining concentration; anddetermining the target odorization concentration based on theodorization concentration standard and the remaining concentration. 10.The method for determining odorization parameters of smart gas devicemanagement of claim 9, wherein the obtaining the remaining concentrationfurther includes: constructing a first feature vector based on theodorization concentration standard and the inspection data; determininga reference feature vector based on the first feature vector;determining a predicted standard deviation of an odorant concentrationafter odorization based on the odorization parameters by processing thereference feature vector through a prediction model, wherein theprediction model is a machine learning model; and determining theremaining concentration based on the predicted standard deviation of theodorant concentration.
 11. The method for determining odorizationparameters of smart gas device management of claim 10, wherein theprediction model is obtained by a training process including: obtaininga plurality of sample data, wherein each sample data is a historicalinspection data on which an odorization operation is performed; andobtaining a cluster center set by clustering the plurality of sampledata; for each of the plurality of sample data, matching a targetcluster center in the cluster center set based on a feature vectorcorresponding to each sample data and using a center vectorcorresponding to the target cluster center as a historical featurevector; determining a standard deviation of the odorant concentrationbased on an odorant concentration distribution feature in the pluralityof sample data; and obtaining the prediction model by training using aplurality of historical feature vectors as a plurality of sets oftraining data and using the standard deviation of the odorantconcentration as a training label.
 12. The method for determiningodorization parameters of smart gas device management of claim 8,wherein the gas data includes one or more of a gas flow, a gas flowrate, a gas concentration, a gas pressure, a gas temperature.
 13. Themethod for determining odorization parameters of smart gas devicemanagement of claim 8, wherein the odorization parameters include one ormore of a type of an odorant, an odorization amount, an odorizationfrequency, an odorization time.
 14. The method for determiningodorization parameters of smart gas device management of claim 8,wherein the inspection data includes one or more of the odorantconcentration in gas, a gas flow rate, a gas concentration, a gaspressure, a gas temperature.
 15. A non-transitory computer-readablestorage medium storing computer instructions, wherein when reading thecomputer instructions in the storage medium, a computer implements themethod for determining the odorization parameters of smart gas devicemanagement of claim 8.